AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts
Article
Article Title | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
---|---|
ERA Journal ID | 211938 |
Article Category | Article |
Authors | Shaik, Thanveer, Tao, Xiaohui, Li, Lin, Xie, Haoran, Dai, Hong-Ning, Zhao, Feng and Yong, Jianming |
Journal Title | Brain Informatics |
Journal Citation | 12 (1) |
Article Number | 14 |
Number of Pages | 18 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2198-4018 |
2198-4026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1186/s40708-025-00262-1 |
Web Address (URL) | https://braininformatics.springeropen.com/articles/10.1186/s40708-025-00262-1 |
Abstract | Purpose Methods Results Conclusions |
Keywords | Behavior patterns; Decision making; Patient monitoring; Reinforcement learning; Vital signs |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Byline Affiliations | School of Mathematics, Physics and Computing |
Wuhan University of Technology, China | |
Lingnan University of Hong Kong, China | |
Hong Kong Baptist University, China | |
Huazhong University of Science and Technology, China | |
School of Business |
https://research.usq.edu.au/item/zyq3w/ai-driven-multi-agent-reinforcement-learning-framework-for-real-time-monitoring-of-physiological-signals-in-stress-and-depression-contexts
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